Source code for banditpylib.learners.mab_learner.ucb

from typing import Optional

import numpy as np

from banditpylib.arms import PseudoArm
from banditpylib.data_pb2 import Context, Actions, Feedback
from .utils import MABLearner


[docs]class UCB(MABLearner): r"""Upper Confidence Bound policy :cite:`auer2002finite` At time :math:`t`, play arm .. math:: \mathrm{argmax}_{i \in \{0, \dots, N-1\}} \left\{ \bar{\mu}_i(t) + \sqrt{ \frac{\alpha \ln(t) }{T_i(t)} } \right\} :param int arm_num: number of arms :param float alpha: alpha :param Optional[str] name: alias name """ def __init__(self, arm_num: int, alpha: float = 2.0, name: Optional[str] = None): super().__init__(arm_num=arm_num, name=name) if alpha <= 0: raise ValueError('Alpha is expected greater than 0. Got %.2f.' % alpha) self.__alpha = alpha def _name(self) -> str: return 'ucb'
[docs] def reset(self): self.__pseudo_arms = [PseudoArm() for arm_id in range(self.arm_num)] # Current time step self.__time = 1
def __UCB(self) -> np.ndarray: """ Returns: optimistic estimate of arms' real means """ ucb = np.array([ arm.em_mean + np.sqrt(self.__alpha * np.log(self.__time) / arm.total_pulls) for arm in self.__pseudo_arms ]) return ucb
[docs] def actions(self, context: Context) -> Actions: del context actions = Actions() arm_pull = actions.arm_pulls.add() if self.__time <= self.arm_num: arm_pull.arm.id = self.__time - 1 else: arm_pull.arm.id = int(np.argmax(self.__UCB())) arm_pull.times = 1 return actions
[docs] def update(self, feedback: Feedback): arm_feedback = feedback.arm_feedbacks[0] self.__pseudo_arms[arm_feedback.arm.id].update( np.array(arm_feedback.rewards)) self.__time += 1